English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a strategy to mitigate the problem of inefficientclustering performance by introducing data augmentation as an auxiliaryplug-in. Classical clustering techniques such as K-means, Gaussian mixturemodel and spectral clustering are central to many data-driven applications.However, recently unsupervised simultaneous feature learning and clusteringusing neural networks also known as Deep Embedded Clustering (DEC) has gainedprominence. Pioneering works on deep feature clustering focus on definingrelevant clustering loss function and choosing the right neural network forextracting features. A central problem in all these cases is data sparsityaccompanied by high intra-class and low inter-class variance, whichsubsequently leads to poor clustering performance and erroneous candidateassignments. Towards this, we employ data augmentation techniques to improvethe density of the clusters, thus improving the overall performance. We train avariant of Convolutional Autoencoder (CAE) with augmented data to construct theinitial feature space as a novel model for deep clustering. We demonstrate theresults of proposed strategy on crowdsourced Indian Heritage dataset. Extensiveexperiments show consistent improvements over existing works.

Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data